To address this, motivated by the tremendous advances in Deep Learning (DL), massive efforts have been recently made to design suitable Neural Network (DNN) architectures  [1]}, [2]}, [3]}. These DL-based approaches have achieved impressive performance in terms of standard performance metrics (precision, recall, and F1-score). Nevertheless, despite their promising results, their suitability in a realistic industrial context still needs further investigation. For that purpose, it is timely to propose an extensive comparison of recent DL techniques that considers the following aspects:
